Mining interesting locations and travel sequences from GPS trajectories

By Yu Zheng, Lizhu Zhang, Xing Xie, Wei-Ying Ma. The Web Conference, 2009

2,148 citations recorded by the source corpus.

Subfields: Geography, Planning and Development, Transportation, Signal Processing

View Paper: https://www.semanticscholar.org/paper/08a8c653b4f20f2b63ac6734f24fa5f5f819782a

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Geography, Planning and Development

1. Yu Zheng
2. Xing Xie
3. Mohamed F. Mokbel
4. Jie Bao
5. Gao Cong
6. Vincent Wenchen Zheng
7. Qiang Yang
8. Hui Xiong

In Transportation

1. Yu Zheng
2. Jie Bao
3. Mohamed F. Mokbel
4. Gao Cong
5. Xing Xie
6. David Wilkie
7. Shan Jiang
8. Michael R. Lyu

In Signal Processing

1. Yu Zheng
2. Xing Xie
3. Mohamed F. Mokbel
4. Jie Bao
5. Gao Cong
6. Qiang Yang
7. David Wilkie
8. Vincent Wenchen Zheng

Citations in the corpus

• Mining significant semantic locations from GPS data (2010, methodology): “Compared with the results of the original HITS [23], L6 and L7 have lower rankings because the weights are normalized.”
• Constructing popular routes from uncertain trajectories (2012, background): “If a tourist would like to travel from to , existing trip planning [1,7,15,17] could explore the sequential relations among these places and derive A C D B by concatenation in Figure 2(b).”
• The shortest path to happiness: recommending beautiful, quiet, and happy routes in the city (2014, background): “([36]) mined such traces by arranging both visited places and mobile users in a bipartite graph, and then ranking places by graph centrality to extract top interesting regions.”
• Learning travel recommendations from user-generated GPS traces (2011, positive): “OVERVIEW 3.1 Application Scenarios The work reported in this article is an important component of our project GeoLife [Zheng et al.”
• Unveiling the complexity of human mobility by querying and mining massive trajectory data (2011, methodology): “It is no surprise, then, that finding answers to these questions is beyond the limits of the current generation of commercial systems, and cannot even be accomplished by simply applying known research prototypes, such as the mobility data mining methods developed within GeoPKDD by the authors of thi”
• Location-based and preference-aware recommendation using sparse geo-social networking data (2012, methodology): “Given a user-specified geospatial range and the user’s WCH, this ap pro ch chooses the topN venues as the final recommendations based on an iterative inference model, which is similar to [29].”
• A Survey on Trajectory Data Management, Analytics, and Learning (2020, neutral): “Human GPS tracking GeoLife [206] 17,621 Human mobility [194]  Urban area Check-in Foursquare [19] Gowalla [39] 104,478 Tourism planning [165] Mobility prediction [58] Online sharing OpenStreetMap [9] 8.”
• A Tale of Many Cities: Universal Patterns in Human Urban Mobility (2011, background): “…the answer of which would lead to many applications, e.g. improve engineered systems such as cloud computing and location-based recommendations [2–5], enhance research in social networks [6–9] and yield insight into a variety of important societal issues, such as urban planning and…”
• Discovering popular routes from trajectories (2011, background): “The search of popular routes in light of past movements is highly relevant to trajectory processing/querying issues, including pattern mining [7], [2], [1], [8], trajectory clustering [9], [10], hot route discovery [11], [12], trajectory prediction [3], [4], [13], etc.”
• GeoLife: A Collaborative Social Networking Service among User, Location and Trajectory (2010, methodology): “Refer to papers [9, 14] for details.”
• Content-Aware Point of Interest Recommendation on Location-Based Social Networks (2015, background): “It was firstly studied on GPS trajectory data (Zheng et al. 2009; Zheng and Xie 2011).”
• Collaborative Filtering Meets Mobile Recommendation: A User-Centered Approach (2010, methodology): “In addition, we also follow (Zheng et al. 2009) to cluster the raw GPS points into 168 meaningful locations for recommendation.”
• Where You Like to Go Next: Successive Point-of-Interest Recommendation (2013, neutral): “One line of research is conducted based on the GPS trajectory logs [Zheng et al. , 2009; 2010a; 2010b; Leung et al. , 2011].”
• Human mobility: Models and applications (2017, background): “Since its release, GeoLife has been used in studies of both human mobility [59, 60] and social interactions [61].”
• Network Representation Learning: A Survey (2017, background): “Recent advancement in network representation learning has proposed new solutions to learn structured embeddings of knowledge bases [69].”